Last updated: 2025-09-18
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Knit directory: SPP1_mouse_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | ff67aa5 | heinin | 2025-09-16 | Updated CellChat plots |
| Rmd | f226445 | heinin | 2025-07-14 | Added lymphoid marker expression |
| html | f226445 | heinin | 2025-07-14 | Added lymphoid marker expression |
| Rmd | fa32c56 | heinin | 2025-02-28 | Analyzing compartments |
| html | fa32c56 | heinin | 2025-02-28 | Analyzing compartments |
| Rmd | 84ba8d7 | heinin | 2025-01-07 | More featureplots |
| html | 84ba8d7 | heinin | 2025-01-07 | More featureplots |
| Rmd | fa70de3 | heinin | 2025-01-07 | Small updates |
| html | fa70de3 | heinin | 2025-01-07 | Small updates |
| Rmd | a3864c6 | heinin | 2025-01-07 | First pass analysis |
| html | a3864c6 | heinin | 2025-01-07 | First pass analysis |
library(workflowr)
library(Seurat)
library(googlesheets4)
library(tidyverse)
library(plyr)
library(UpSetR)
library(ggrepel)
library(enrichR)
library(patchwork)
library(biomaRt)
library(scProportionTest)
setwd("/home/hnatri/SPP1_mouse_scRNAseq/")
set.seed(1234)
options(future.globals.maxSize = 30000 * 1024^2)
reduction <- "integratedSCTumap"
source("/home/hnatri/SPP1_mouse_scRNAseq/code/CART_plot_functions.R")
source("/home/hnatri/SPP1_mouse_scRNAseq/code/colors_themes.R")
source("/home/hnatri/SingleCellBestPractices/scripts/preprocessing_qc_module.R")
source("/home/hnatri/SingleCellBestPractices/scripts/integration_module.R")
# Cluster annotations
gs4_deauth()
cluster_annot <- gs4_get("https://docs.google.com/spreadsheets/d/127J6C4KF7uBGKUnrPuC1mcsb_wNCN6k1zXKSCbJ6q0M/edit?usp=sharing")
cluster_annot <- read_sheet(cluster_annot, sheet = "Cluster annotation")
Core ran CCA integration
seurat_data <- readRDS("/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/ROUND1/Seurat.rds")
unique(seurat_data$orig.ident)
DimPlot(seurat_data, reduction = "integrated.cca")
| Version | Author | Date |
|---|---|---|
| a3864c6 | heinin | 2025-01-07 |
VlnPlot(seurat_data, features = c("nCount_RNA", "nFeature_RNA", "percent.mt"),
pt.size = 0)
| Version | Author | Date |
|---|---|---|
| a3864c6 | heinin | 2025-01-07 |
par(mfrow=c(2,3))
# PLOTS 1 & 2: nCount vs. nFeature
smoothScatter(log2(seurat_data$nCount_RNA), log2(seurat_data$nCount_RNA),
xlab = "log2(nCount_RNA)", ylab = "log2(nFeature_RNA)")
smoothScatter(seurat_data$nCount_RNA, seurat_data$nCount_RNA,
xlab = "nCount_RNA", ylab = "nFeature_RNA")
# PLOTS 3 & 4: nCount vs. percent.mt_RNA
smoothScatter(seurat_data$percent.mt, log2(seurat_data$nCount_RNA),
xlab = "% MT", ylab = "log2(nCount_RNA)")
smoothScatter(seurat_data$percent.mt, seurat_data$nCount_RNA,
xlab = "% MT", ylab = "nCount_RNA")
abline(v = 10, h = 1000,
lty = "dashed", lwd = 1.25, col = "red")
# PLOTS 5 & 6: nFeature vs. percent.mt_RNA
smoothScatter(seurat_data$percent.mt, log2(seurat_data$nFeature_RNA),
xlab = "% MT", ylab = "log2(nFeature_RNA)")
smoothScatter(seurat_data$percent.mt, seurat_data$nFeature_RNA,
xlab = "% MT", ylab = "nFeature_RNA")
abline(v = 10, h = 500,
lty = "dashed", lwd = 1.25, col = "red")
seurat_data <- subset(seurat_data, subset = nFeature_RNA > 500 & nCount_RNA > 1000)
# Normalizing and scaling
seurat_data <- SCTransform(seurat_data,
variable.features.n = 1000,
#vars.to.regress = c(""),
vst.flavor = "v2",
return.only.var.genes = T,
verbose = F)
# Adding cell cycle scores
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes
# Converting human gene names to mouse
mouse_human_genes <- read.table("http://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt",
sep="\t", header = T)
mouse <- split.data.frame(mouse_human_genes,mouse_human_genes$Common.Organism.Name)[[2]]
human <- split.data.frame(mouse_human_genes,mouse_human_genes$Common.Organism.Name)[[1]]
mouse <- mouse[,c(1,4)]
human <- human[,c(1,4)]
mh_data <- merge.data.frame(mouse, human, by = "DB.Class.Key",all.y = TRUE)
s.genes <- mh_data %>% filter(Symbol.y %in% s.genes) %>%
dplyr::select(Symbol.x) %>% unlist() %>% as.character()
g2m.genes <- mh_data %>% filter(Symbol.y %in% g2m.genes) %>%
dplyr::select(Symbol.x) %>% unlist() %>% as.character()
seurat_data <- CellCycleScoring(seurat_data,
s.features = s.genes,
g2m.features = g2m.genes,
set.ident = F)
### Rerunning integration
vars_to_regress <- c("G2M.Score",
"S.Score",
"nCount_RNA",
"nFeature_RNA",
"percent.mt")
DefaultAssay(seurat_data) <- "RNA"
seurat_data <- JoinLayers(seurat_data)
seurat_list <- SplitObject(seurat_data, split.by = "orig.ident")
# Unable to run SoupX: no raw data files
# Calling doublets
seurat_list <- run_sctransform(seurat_list)
seurat_list <- run_doubletfinder(sample_seurat_list = seurat_list,
manual_dbr = 0.15)
# Removing doublets
seurat_list <- lapply(seurat_list, function(xx){
subset(xx, subset = doublet_finder == "Singlet")
})
# Integrating
seurat_list <- run_sctransform(seurat_list,
vars_to_regress = vars_to_regress)
integrated_data <- sct_rpca_integration(seurat_list,
ndims = 8)
saveRDS(integrated_data, "/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/scRNAseq_Seurat_dim8_nCount1k_nFeature500_dblrate15.rds")
# range(integrated_data$nCount_RNA)
# 500 122405
# range(integrated_data$nFeature_RNA)
# 217 8992
integrated_data <- readRDS("/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/scRNAseq_Seurat_dim8_nCount1k_nFeature500_dblrate15.rds")
integrated_data$cluster <- integrated_data$integratedSCTsnn_res.0.3
table(integrated_data$cluster)
range(integrated_data$nCount_RNA)
range(integrated_data$nFeature_RNA)
DimPlot(integrated_data,
group.by = "cluster",
cols = carspp1_cluster_col,
reduction = reduction,
label = T,
label.box = T,
label.size = 3,
repel = T,
raster = T,
raster.dpi = c(1024, 1024),
pt.size = 3) +
ggtitle("integratedSCTsnn_res.0.3") +
theme_classic() +
NoLegend() +
NoAxes() +
coord_fixed(1)
| Version | Author | Date |
|---|---|---|
| a3864c6 | heinin | 2025-01-07 |
DimPlot(integrated_data,
group.by = "orig.ident",
#cols = jak1_celltype_col,
reduction = reduction,
#label = T,
#label.box = T,
#label.size = 3,
#repel = T,
raster = T,
raster.dpi = c(1024, 1024),
pt.size = 3) +
ggtitle("Sample") +
theme_classic() +
#NoLegend() +
NoAxes() +
coord_fixed(1)
| Version | Author | Date |
|---|---|---|
| a3864c6 | heinin | 2025-01-07 |
DimPlot(integrated_data,
group.by = "Phase",
#cols = jak1_celltype_col,
reduction = reduction,
#label = T,
#label.box = T,
#label.size = 3,
#repel = T,
raster = T,
raster.dpi = c(1024, 1024),
pt.size = 3) +
ggtitle("Phase") +
theme_classic() +
#NoLegend() +
NoAxes() +
coord_fixed(1)
| Version | Author | Date |
|---|---|---|
| a3864c6 | heinin | 2025-01-07 |
DimPlot(integrated_data,
group.by = "cluster",
split.by = "orig.ident",
cols = carspp1_cluster_col,
reduction = reduction,
ncol = 2,
label = T,
label.box = T,
label.size = 3,
repel = T,
raster = T,
raster.dpi = c(1024, 1024),
pt.size = 3) +
ggtitle("") +
theme_classic() +
NoLegend() +
NoAxes() +
coord_fixed(1)
| Version | Author | Date |
|---|---|---|
| a3864c6 | heinin | 2025-01-07 |
# Converting mouse gene names to human
mouse_human_genes <- read.csv("http://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt", sep="\t")
convert_mouse_to_human <- function(gene_list){
gene_names <- as.data.frame(matrix(nrow = length(gene_list),
ncol = 2))
colnames(gene_names) <- c("mouse", "human")
rownames(gene_names) <- gene_list
gene_names$mouse <- gene_list
for(gene in gene_list){
class_key = (mouse_human_genes %>% filter(Symbol == gene & Common.Organism.Name=="mouse, laboratory"))[['DB.Class.Key']]
if(!identical(class_key, integer(0)) ){
human_genes = (mouse_human_genes %>% filter(DB.Class.Key == class_key & Common.Organism.Name=="human"))[,"Symbol"]
if(length(human_genes)==0){
gene_names[gene, "human"] <- NA
} else if (length(human_genes)>1){
# human_genes <- paste0(human_genes, collapse = ", ")
bind_df <- data.frame("mouse" = rep(gene, times = length(human_genes)),
"human" = human_genes)
gene_names <- rbind(gene_names, bind_df)
} else {
gene_names[gene, "human"] <- human_genes
}
}
}
return(gene_names)
}
gene_names <- convert_mouse_to_human(rownames(integrated_data@assays$RNA))
length(rownames(integrated_data@assays$RNA))
dim(gene_names)
# Keeping mouse genes with a single human ortholog
gene_names <- gene_names %>%
group_by(mouse) %>%
filter(!is.na(human),
n() == 1) %>%
ungroup()
DefaultAssay(integrated_data) <- "RNA"
integrated_data <- JoinLayers(integrated_data)
assay_data <- GetAssayData(integrated_data, slot = "counts")
assay_data <- assay_data[which(rownames(assay_data) %in% gene_names$mouse),]
new_names <- rownames(assay_data)
new_names <- mapvalues(x = new_names,
from = gene_names$mouse,
to = gene_names$human)
rownames(assay_data) <- new_names
integrated_data[["RNA_human"]] <- CreateAssayObject(assay_data,
min.cells = 0,
min.features = 0)
DefaultAssay(integrated_data) <- "RNA_human"
integrated_data <- NormalizeData(integrated_data)
saveRDS(integrated_data, "/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/scRNAseq_Seurat_dim8_nCount1k_nFeature500_dblrate15.rds")
FeaturePlot(integrated_data,
#layer = "RNA",
slot = "data",
features = c("PTPRC", "CD3E", "CD4", "CD8A", "CD19", "ITGAM",
"CD36", "CD14", "SPP1", "C1QA", "C1QB", "C1QC",
"APOE", "SPP1"),
order = T,
ncol = 5,
reduction = reduction,
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend()
FeaturePlot(integrated_data,
#layer = "RNA",
slot = "data",
features = c("G2M.Score", "S.Score", "nCount_RNA", "nFeature_RNA", "percent.mt"),
order = T,
ncol = 3,
reduction = reduction,
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend()
| Version | Author | Date |
|---|---|---|
| a3864c6 | heinin | 2025-01-07 |
count_data <- LayerData(seurat_data, assay = "RNA_human", layer = "counts")
scImmuCC_Layered(count = count_data, Non_Immune = FALSE)
# Importing results
scicc_labels <- read.csv("/home/hnatri/PD1_mm/docs/Layer1_scImmuCC_label.csv",
row.names = "X")
length(colnames(seurat_data))
length(intersect(scicc_labels$barcodes, colnames(seurat_data)))
seurat_data$scImmuCC_celltype <- mapvalues(x = colnames(seurat_data),
from = scicc_labels$barcodes,
to = scicc_labels$cell_type)
# Plotting
DimPlot(seurat_data,
group.by = "scImmuCC_celltype",
reduction = "umap",
raster = T,
#cols = scImmuCC_celltype_col,
label = T) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend() &
manuscript_theme
#saveRDS(seurat_data, "/tgen_labs/banovich/BCTCSF/PD1_mm_Seurat/PD1_mm_Seurat_merged.Rds")
unique(integrated_data$cluster)
carspp1_cluster_col
markers <- presto::wilcoxauc(integrated_data,
group_by = "cluster",
assay = "data",
seurat_assay = "RNA_human")
#markers <- markers[-which(markers$feature %in% grep("MT-", markers$feature, value = T)),]
#markers <- markers[-which(markers$feature %in% grep("^RP", markers$feature, value = T)),]
markers_sig <- markers %>%
filter(padj < 0.01,
abs(logFC) > 1)
write.table(markers_sig, "/home/hnatri/SPP1_mouse_scRNAseq/cluster_sig_top_markers_nCount1k_nFeat500_dblrate15.tsv", quote = F, row.names = F, sep = "\t")
plot_features <- markers %>% group_by(group) %>% slice_max(order_by = auc, n = 5)
create_dotplot_heatmap(seurat_object = integrated_data,
plot_features = unique(plot_features$feature),
group_var = "cluster",
group_colors = carspp1_cluster_col,
column_title = "",
row_km = 5,
col_km = 5,
row.order = NULL,
col.order = NULL)
plot_features <- c("PTMA", "PFN1", "CFL1", "TMSB4X", "TPT1", "TMSB10", "MIF",
"PDPN", "NLRP3", "IL1B", "CCL4", "S100A8", "S100A9",
"S100A10", "TYROBP", "CD68", "ICAM1", "C1QA", "C1QB", "C1QC",
"CD74", "AREG", "CD4", "APOE", "FABP5", "SPP1", "CD274",
"CD96", "PTPRC", "CEMIP2", "KLRD1", "CD8A", "NKG7", "IL32",
"CD3D", "BTG1", "IFITM2", "ITM2A", "SELL", "GZMB", "CD79A",
"ACTA2", "PDGFRB", "COL1A1", "CD163", "MRC1", "ITGAM", "CD14",
"CD279", "PDCD1", "TREM2", "TMEM119", "P2RY12", "CX3CR1",
"CD19")
create_dotplot_heatmap(seurat_object = integrated_data,
plot_features = plot_features,
group_var = "cluster",
group_colors = carspp1_cluster_col,
column_title = "",
row_km = 5,
col_km = 5,
row.order = NULL,
col.order = NULL)
FeaturePlot(integrated_data,
#layer = "RNA",
slot = "data",
features = plot_features,
order = T,
ncol = 5,
reduction = reduction,
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend()
Idents(integrated_data) <- integrated_data$integratedSCTsnn_res.0.3
integrated_data <- FindSubCluster(integrated_data,
cluster = 8,
graph.name = "integratedSCTsnn",
subcluster.name = "sub.cluster",
resolution = 0.1,
algorithm = 1)
DimPlot(integrated_data,
group.by = "sub.cluster",
#cols = carspp1_celltype_col,
reduction = reduction,
label = T,
label.box = T,
label.size = 3,
repel = T,
raster = T,
raster.dpi = c(1024, 1024),
pt.size = 3) +
ggtitle("") +
theme_classic() +
NoLegend() +
NoAxes() +
coord_fixed(1)
integrated_data$annot <- mapvalues(x = integrated_data$sub.cluster,
from = cluster_annot$sub.cluster,
to = cluster_annot$annot)
DimPlot(integrated_data,
group.by = "annot",
cols = carspp1_celltype_col,
reduction = reduction,
label = T,
label.box = T,
label.size = 3,
repel = T,
raster = T,
raster.dpi = c(1024, 1024),
pt.size = 3) +
ggtitle("") +
theme_classic() +
NoLegend() +
NoAxes() +
coord_fixed(1)
#saveRDS(integrated_data, "/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/scRNAseq_Seurat_dim8.rds")
create_barplot(integrated_data,
group_var = "orig.ident",
plot_var = "annot",
plot_levels = levels(integrated_data$annot),
group_levels = sort(unique(integrated_data$orig.ident)),
plot_colors = carspp1_celltype_col,
var_names = c("Frequency", ""),
legend_title = "")
Using scProportionTest. The first group (for example, CAR in “CAR vs. CTRL”) gets positive values.
prop_test <- sc_utils(integrated_data)
# Permutation testing and bootstrapping
# CAR vs. CTRL
prop_test <- permutation_test(
prop_test, cluster_identity = "annot",
sample_2 = "CAR", sample_1 = "TUMOR",
sample_identity = "orig.ident")
perm_plot <- permutation_plot(prop_test)
perm_plot + scale_colour_manual(values = c("tomato", "azure2")) + NoLegend() + ggtitle("CAR vs. CTRL")
# SPP1 vs. CTRL
prop_test <- permutation_test(
prop_test, cluster_identity = "annot",
sample_2 = "SPP1", sample_1 = "TUMOR",
sample_identity = "orig.ident")
perm_plot <- permutation_plot(prop_test)
perm_plot + scale_colour_manual(values = c("tomato", "azure2")) + NoLegend() + ggtitle("SPP1 vs. CTRL")
# CAR+SPP1 vs. CTRL
prop_test <- permutation_test(
prop_test, cluster_identity = "annot",
sample_2 = "SPP1+CAR", sample_1 = "TUMOR",
sample_identity = "orig.ident")
perm_plot <- permutation_plot(prop_test)
perm_plot + scale_colour_manual(values = c("tomato", "azure2")) + NoLegend() + ggtitle("SPP1+CAR vs. CTRL")
# CAR+SPP1 vs. CAR
prop_test <- permutation_test(
prop_test, cluster_identity = "annot",
sample_2 = "SPP1+CAR", sample_1 = "CAR",
sample_identity = "orig.ident")
perm_plot <- permutation_plot(prop_test)
perm_plot + scale_colour_manual(values = c("tomato", "azure2")) + NoLegend() + ggtitle("SPP1+CAR vs. CAR")
# CAR+SPP1 vs. SPP1
prop_test <- permutation_test(
prop_test, cluster_identity = "annot",
sample_2 = "SPP1+CAR", sample_1 = "SPP1",
sample_identity = "orig.ident")
perm_plot <- permutation_plot(prop_test)
perm_plot + scale_colour_manual(values = c("tomato", "azure2")) + NoLegend() + ggtitle("SPP1+CAR vs. SPP1")
# To build on command line, run Rscript -e "rmarkdown::render('first_pass.Rmd')"
# Then "mv *.html /home/hnatri/SPP1_mouse_scRNAseq/docs/"
sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] parallel stats4 grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] sctransform_0.4.1 ROCR_1.0-11
[3] KernSmooth_2.23-21 fields_14.1
[5] spam_2.9-1 mclust_6.0.0
[7] scCustomize_1.1.1 glmGamPoi_1.14.3
[9] DoubletFinder_2.0.4 scater_1.30.1
[11] scuttle_1.12.0 SingleCellExperiment_1.24.0
[13] SummarizedExperiment_1.32.0 Biobase_2.62.0
[15] GenomicRanges_1.54.1 GenomeInfoDb_1.38.5
[17] IRanges_2.36.0 S4Vectors_0.40.2
[19] BiocGenerics_0.48.1 MatrixGenerics_1.14.0
[21] matrixStats_1.0.0 SoupX_1.6.2
[23] circlize_0.4.15 ComplexHeatmap_2.18.0
[25] viridis_0.6.3 viridisLite_0.4.2
[27] RColorBrewer_1.1-3 scProportionTest_0.0.0.9000
[29] biomaRt_2.58.2 patchwork_1.1.2
[31] enrichR_3.2 ggrepel_0.9.3
[33] UpSetR_1.4.0 plyr_1.8.8
[35] lubridate_1.9.2 forcats_1.0.0
[37] stringr_1.5.0 dplyr_1.1.2
[39] purrr_1.0.1 readr_2.1.4
[41] tidyr_1.3.0 tibble_3.2.1
[43] ggplot2_3.4.2 tidyverse_2.0.0
[45] googlesheets4_1.1.0 Seurat_5.0.1
[47] SeuratObject_5.0.1 sp_1.6-1
[49] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.2 spatstat.sparse_3.0-1
[3] bitops_1.0-7 httr_1.4.6
[5] doParallel_1.0.17 tools_4.3.0
[7] utf8_1.2.3 R6_2.5.1
[9] lazyeval_0.2.2 uwot_0.1.14
[11] GetoptLong_1.0.5 withr_2.5.0
[13] prettyunits_1.1.1 gridExtra_2.3
[15] progressr_0.13.0 cli_3.6.1
[17] Cairo_1.6-0 spatstat.explore_3.2-1
[19] fastDummies_1.7.3 labeling_0.4.2
[21] sass_0.4.6 spatstat.data_3.0-1
[23] ggridges_0.5.4 pbapply_1.7-0
[25] parallelly_1.36.0 WriteXLS_6.4.0
[27] maps_3.4.1 rstudioapi_0.14
[29] RSQLite_2.3.1 generics_0.1.3
[31] shape_1.4.6 ica_1.0-3
[33] spatstat.random_3.1-5 Matrix_1.6-5
[35] ggbeeswarm_0.7.2 fansi_1.0.4
[37] abind_1.4-5 lifecycle_1.0.3
[39] whisker_0.4.1 yaml_2.3.7
[41] snakecase_0.11.0 SparseArray_1.2.3
[43] BiocFileCache_2.10.2 Rtsne_0.16
[45] paletteer_1.5.0 blob_1.2.4
[47] promises_1.2.0.1 crayon_1.5.2
[49] miniUI_0.1.1.1 lattice_0.21-8
[51] beachmat_2.18.1 cowplot_1.1.1
[53] KEGGREST_1.42.0 magick_2.7.4
[55] pillar_1.9.0 knitr_1.43
[57] rjson_0.2.21 future.apply_1.11.0
[59] codetools_0.2-19 leiden_0.4.3
[61] glue_1.6.2 getPass_0.2-4
[63] data.table_1.14.8 vctrs_0.6.2
[65] png_0.1-8 cellranger_1.1.0
[67] gtable_0.3.3 rematch2_2.1.2
[69] cachem_1.0.8 xfun_0.39
[71] S4Arrays_1.2.0 mime_0.12
[73] survival_3.5-5 gargle_1.4.0
[75] iterators_1.0.14 ellipsis_0.3.2
[77] fitdistrplus_1.1-11 nlme_3.1-162
[79] bit64_4.0.5 progress_1.2.2
[81] filelock_1.0.2 RcppAnnoy_0.0.20
[83] rprojroot_2.0.3 bslib_0.4.2
[85] irlba_2.3.5.1 vipor_0.4.5
[87] colorspace_2.1-0 DBI_1.1.3
[89] ggrastr_1.0.2 tidyselect_1.2.0
[91] processx_3.8.1 bit_4.0.5
[93] compiler_4.3.0 curl_5.0.0
[95] git2r_0.32.0 BiocNeighbors_1.20.2
[97] xml2_1.3.4 DelayedArray_0.28.0
[99] plotly_4.10.2 scales_1.2.1
[101] lmtest_0.9-40 callr_3.7.3
[103] rappdirs_0.3.3 digest_0.6.31
[105] goftest_1.2-3 presto_1.0.0
[107] spatstat.utils_3.0-3 rmarkdown_2.22
[109] XVector_0.42.0 htmltools_0.5.5
[111] pkgconfig_2.0.3 sparseMatrixStats_1.14.0
[113] highr_0.10 dbplyr_2.3.2
[115] fastmap_1.1.1 rlang_1.1.1
[117] GlobalOptions_0.1.2 htmlwidgets_1.6.2
[119] DelayedMatrixStats_1.24.0 shiny_1.7.4
[121] farver_2.1.1 jquerylib_0.1.4
[123] zoo_1.8-12 jsonlite_1.8.5
[125] BiocParallel_1.36.0 BiocSingular_1.18.0
[127] RCurl_1.98-1.12 magrittr_2.0.3
[129] GenomeInfoDbData_1.2.11 dotCall64_1.0-2
[131] munsell_0.5.0 Rcpp_1.0.10
[133] reticulate_1.29 stringi_1.7.12
[135] zlibbioc_1.48.0 MASS_7.3-60
[137] listenv_0.9.0 deldir_1.0-9
[139] Biostrings_2.70.1 splines_4.3.0
[141] tensor_1.5 hms_1.1.3
[143] ps_1.7.5 igraph_1.4.3
[145] spatstat.geom_3.2-1 RcppHNSW_0.5.0
[147] ScaledMatrix_1.10.0 reshape2_1.4.4
[149] XML_3.99-0.14 evaluate_0.21
[151] ggprism_1.0.4 tzdb_0.4.0
[153] foreach_1.5.2 httpuv_1.6.11
[155] RANN_2.6.1 polyclip_1.10-4
[157] future_1.32.0 clue_0.3-64
[159] scattermore_1.2 janitor_2.2.0
[161] rsvd_1.0.5 xtable_1.8-4
[163] RSpectra_0.16-1 later_1.3.1
[165] googledrive_2.1.0 beeswarm_0.4.0
[167] memoise_2.0.1 AnnotationDbi_1.64.1
[169] cluster_2.1.4 timechange_0.2.0
[171] globals_0.16.2